Advances and Applications of Distributed Optical Fiber Sensing (DOFS) in Multi-scales Geoscience Problems

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Innovative sensing used to monitor geohazards, energy geosciences & critical zone in diverse and harsh environments.

Distributed acoustic sensing (DAS) is a rapidly developing technology with a wide range of applications. It is being used to obtain high-quality seismic data in extreme environments, monitor traffic, and acquire high-resolution data in complex environments. DAS is also being used to improve the signal-to-noise ratio of vertical seismic profile (VSP) surveys, reconstruct data with high precision and high SNR, and track train position, speed, and number of trains. A novel multi-scale dense-connection denoising network (MDD-Net) has been proposed to achieve high-accuracy processing of the complex DAS background noise and restore desired signals.

In addition, a unified real-time object detection algorithm has been developed to estimate traffic flow and vehicle speed in DAS data. A novel algorithm based on U-net in combination with the Hankel matrix as input/output has been developed to reconstruct data with high precision and high SNR. An attention-guided technique has been proposed to improve the signal-to-noise ratio (SNR) of DAS seismic data. A multi-scale interactive convolutional neural network (MSI-Net) has been proposed to denoise the challenging DAS seismic data. Finally, a three-station interferometry method has been developed to improve the noise cross-correlation functions of the linear array and suppress the precursor signal.

  • Distributed acoustic sensing (DAS) is being used to obtain high-quality seismic data in extreme environments, monitor traffic, and acquire high-resolution data in complex environments.
  • A novel multi-scale dense-connection denoising network (MDD-Net) has been proposed to achieve high-accuracy processing of the complex DAS background noise and restore desired signals.
  • A unified real-time object detection algorithm has been developed to estimate traffic flow and vehicle speed in DAS data.
  • A novel algorithm based on U-net in combination with the Hankel matrix as input/output has been developed to reconstruct data with high precision and high SNR.
  • An attention-guided technique has been proposed to improve the signal-to-noise ratio (SNR) of DAS seismic data.
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